Euler–Lagrange equation

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In the calculus of variations and classical mechanics, the Euler–Lagrange equations[1] are a system of second-order ordinary differential equations whose solutions are stationary points of the given action functional. The equations were discovered in the 1750s by Swiss mathematician Leonhard Euler and Italian mathematician Joseph-Louis Lagrange.

Because a differentiable functional is stationary at its local extrema, the Euler–Lagrange equation is useful for solving optimization problems in which, given some functional, one seeks the function minimizing or maximizing it. This is analogous to Fermat's theorem in calculus, stating that at any point where a differentiable function attains a local extremum its derivative is zero. In Lagrangian mechanics, according to Hamilton's principle of stationary action, the evolution of a physical system is described by the solutions to the Euler equation for the action of the system. In this context Euler equations are usually called Lagrange equations. In classical mechanics,[2] it is equivalent to Newton's laws of motion; indeed, the Euler-Lagrange equations will produce the same equations as Newton's Laws. This is particularly useful when analyzing systems whose force vectors are particularly complicated. It has the advantage that it takes the same form in any system of generalized coordinates, and it is better suited to generalizations. In classical field theory there is an analogous equation to calculate the dynamics of a field.


The Euler–Lagrange equation was developed in the 1750s by Euler and Lagrange in connection with their studies of the tautochrone problem. This is the problem of determining a curve on which a weighted particle will fall to a fixed point in a fixed amount of time, independent of the starting point.

Lagrange solved this problem in 1755 and sent the solution to Euler. Both further developed Lagrange's method and applied it to mechanics, which led to the formulation of Lagrangian mechanics. Their correspondence ultimately led to the calculus of variations, a term coined by Euler himself in 1766.[3]


Let be a real dynamical system with degrees of freedom. Here is the configuration space and the Lagrangian, i.e. a smooth real-valued function such that and is an -dimensional "vector of speed". (For those familiar with differential geometry, is a smooth manifold, and where is the tangent bundle of

Let be the set of smooth paths for which and

The action functional is defined via

A path is a stationary point of if and only if

Here, is the time derivative of When we say stationary point, we mean a stationary point of with respect to any small perturbation in . See proofs below for more rigorous detail.

Derivation of the one-dimensional Euler–Lagrange equation

The derivation of the one-dimensional Euler–Lagrange equation is one of the classic proofs in mathematics. It relies on the fundamental lemma of calculus of variations.

We wish to find a function which satisfies the boundary conditions , , and which extremizes the functional

We assume that is twice continuously differentiable.[4] A weaker assumption can be used, but the proof becomes more difficult.[citation needed]

If extremizes the functional subject to the boundary conditions, then any slight perturbation of that preserves the boundary values must either increase (if is a minimizer) or decrease (if is a maximizer).

Let be the result of such a perturbation of , where is small and is a differentiable function satisfying . Then define

We now wish to calculate the total derivative of with respect to ε.

The third line follows from the fact that does not depend on , i.e. .

When , has an extremum value, so that

The next step is to use integration by parts on the second term of the integrand, yielding

Using the boundary conditions ,

Applying the fundamental lemma of calculus of variations now yields the Euler–Lagrange equation

Alternative derivation of the one-dimensional Euler–Lagrange equation

Given a functional

on with the boundary conditions and , we proceed by approximating the extremal curve by a polygonal line with segments and passing to the limit as the number of segments grows arbitrarily large.

Divide the interval into equal segments with endpoints and let . Rather than a smooth function we consider the polygonal line with vertices , where and . Accordingly, our functional becomes a real function of variables given by

Extremals of this new functional defined on the discrete points correspond to points where

Note that change of affects L not only at m but also at m-1 for the derivative of the 3rd argument.

Evaluating the partial derivative gives

Dividing the above equation by gives

and taking the limit as of the right-hand side of this expression yields

The left hand side of the previous equation is the functional derivative of the functional . A necessary condition for a differentiable functional to have an extremum on some function is that its functional derivative at that function vanishes, which is granted by the last equation.


A standard example[citation needed] is finding the real-valued function y(x) on the interval [a, b], such that y(a) = c and y(b) = d, for which the path length along the curve traced by y is as short as possible.

the integrand function being .

The partial derivatives of L are:

By substituting these into the Euler–Lagrange equation, we obtain

that is, the function must have a constant first derivative, and thus its graph is a straight line.


Single function of single variable with higher derivatives[edit]

The stationary values of the functional

can be obtained from the Euler–Lagrange equation[5]

under fixed boundary conditions for the function itself as well as for the first derivatives (i.e. for all ). The endpoint values of the highest derivative remain flexible.

Several functions of single variable with single derivative[edit]

If the problem involves finding several functions () of a single independent variable () that define an extremum of the functional

then the corresponding Euler–Lagrange equations are[6]

Single function of several variables with single derivative[edit]

A multi-dimensional generalization comes from considering a function on n variables. If is some surface, then

is extremized only if f satisfies the partial differential equation

When n = 2 and functional is the energy functional, this leads to the soap-film minimal surface problem.

Several functions of several variables with single derivative[edit]

If there are several unknown functions to be determined and several variables such that

the system of Euler–Lagrange equations is[5]

Single function of two variables with higher derivatives[edit]

If there is a single unknown function f to be determined that is dependent on two variables x1 and x2 and if the functional depends on higher derivatives of f up to n-th order such that

then the Euler–Lagrange equation is[5]

which can be represented shortly as:

wherein are indices that span the number of variables, that is, here they go from 1 to 2. Here summation over the indices is only over in order to avoid counting the same partial derivative multiple times, for example appears only once in the previous equation.

Several functions of several variables with higher derivatives[edit]

If there are p unknown functions fi to be determined that are dependent on m variables x1 ... xm and if the functional depends on higher derivatives of the fi up to n-th order such that

where are indices that span the number of variables, that is they go from 1 to m. Then the Euler–Lagrange equation is

where the summation over the is avoiding counting the same derivative several times, just as in the previous subsection. This can be expressed more compactly as

Generalization to manifolds[edit]

Let be a smooth manifold, and let denote the space of smooth functions . Then, for functionals of the form

where is the Lagrangian, the statement is equivalent to the statement that, for all , each coordinate frame trivialization of a neighborhood of yields the following equations:

Euler-Lagrange equations can also be written in a coordinate-free form as [7]

where is the canonical momenta 1-form corresponding to the Lagrangian . The vector field generating time translations is denoted by and the Lie derivative is denoted by . One can use local charts in which and and use coordinate expressions for the Lie derivative to see equivalence with coordinate expressions of the Euler Lagrange equation. The coordinate free form is particularly suitable for geometrical interpretation of the Euler Lagrange equations.

See also[edit]


  1. ^ Fox, Charles (1987). An introduction to the calculus of variations. Courier Dover Publications. ISBN 978-0-486-65499-7.
  2. ^ Goldstein, H.; Poole, C.P.; Safko, J. (2014). Classical Mechanics (3rd ed.). Addison Wesley.
  3. ^ A short biography of Lagrange Archived 2007-07-14 at the Wayback Machine
  4. ^ Courant & Hilbert 1953, p. 184
  5. ^ a b c Courant, R; Hilbert, D (1953). Methods of Mathematical Physics. Vol. I (First English ed.). New York: Interscience Publishers, Inc. ISBN 978-0471504474.
  6. ^ Weinstock, R. (1952). Calculus of Variations with Applications to Physics and Engineering. New York: McGraw-Hill.
  7. ^ José; Saletan (1998). "Classical Dynamics: A contemporary approach". Cambridge University Press. ISBN 9780521636360. Retrieved 2023-09-12.